Team Planet - Space Apps Challenge

Team Planet| Sustaining Our Planet for Future Generations

Awards & Nominations

Team Planet has received the following awards and nominations. Way to go!

Local Peoples' Choice Winner

Sustaining Our Planet for Future Generations

There is concern worldwide that environmental issues we face today will have an impact on future generations. Your challenge is to create a way to communicate the importance of environmental responsibility to people of all ages.

Project Space Watch

Summary

SummaryThe purpose of Project Space Watch is to watch and analyse the ground situations of earth using satellite imagery for different sustainable aspects and predict the future situations of earth via utilising machine learning tools. By forecasting future severity rates (via our “Critical Data-Map Index”), the models points us towards a better balance among Society, Economy and Environment to ensure the sustainability of planet. As for the time constraints, we can currently process the satellite observation datasets, analyse and visualise the in-depth aspects of the satellite imagery.

How We Addressed This Challenge

In order to gain insight into the ground factors that were most essential to have broader picture of the ground situations, we focused on gathering diverse datasets, encompassing demographics, Nasa/jaxa/esa data, earth science data, government policies, Geo-information data from Earth-Explorer among other sources. We then used the Data science/ ML tools to process the satellite observation datasets, analyse and visualise the in-depth aspects of the satellite imagery.

Among the others, further development would be done for training models used to predict future “Critical Data-Map Index”, which forecasts future critical aspects on environment at the local, regional and global level . This would allow citizens, industry and governments to take appropriate action ahead of time while avoiding the serious environmental, societal and economical consequences. In other words, the model’s main benefit is that it would be specific enough to pinpoint different planetary environmental factors, broadening the horizon of peoples across all ages and demographics, industries and government authorities so appropriate actions are/could be taken.

How We Developed This Project

At the global scale, scientific data now indicates that humans are living beyond the carrying capacity of planet Earth and that this cannot continue indefinitely. Because of these unfortunate factors, we wanted to use our knowledge to make a positive and substantial impact on the worldwide fight against the Sustainability barriers of the planet.

We extracted the satellite observatory datasets from different sources.

For the Machine Learning Analysis and Visualisation part.

· Tools used were Jupyter Notebook.

· Libraries used were Numpy, Matplotlib, Rasterio.

[Rasterio provides an easy to use API for processing satellite imagery.]

For easy UI interface to convey the message, the prototype is also deployed . It is currently under initial development.

· Tools used were HTML, CSS, JS, Django and SQLite3.

For the future developments, we would gather training datasets from different sources, develop a machine learning model, train the model with the gathered available data and then deploy to forecast the models to show the current and future critical situations of sustainability aspects if any, of our planet for more effective planning and execution by all authorities to avoid the critical stage.

How We Used Space Agency Data in This Project

We used the open source of USGS Earth Explorer and also of Nasa's partner agencies to extract the satellite image datasets for the analysis using ML tools. Except for that NASA and other data sources has been used at most of this project for documentation, referencing, templates to work under sustainability and so on.